Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach

31 January 2018

Papers citing "Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach"

46 / 96 papers shown

Title
Training a Resilient Q-Network against Observational Interference Chao-Han Huck Yang I-Te Danny Hung Ouyang Yi Pin-Yu Chen OOD 26 14 0 18 Feb 2021
Increasing the Confidence of Deep Neural Networks by Coverage Analysis Giulio Rossolini Alessandro Biondi Giorgio Buttazzo AAML 26 13 0 28 Jan 2021
ROBY: Evaluating the Robustness of a Deep Model by its Decision Boundaries Jinyin Chen Zhen Wang Haibin Zheng Jun Xiao Zhaoyan Ming AAML 19 5 0 18 Dec 2020
Recent Advances in Understanding Adversarial Robustness of Deep Neural Networks Tao Bai Jinqi Luo Jun Zhao AAML 49 8 0 03 Nov 2020
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? Anna-Kathrin Kopetzki Bertrand Charpentier Daniel Zügner Sandhya Giri Stephan Günnemann 23 45 0 28 Oct 2020
Planning with Learned Dynamics: Probabilistic Guarantees on Safety and Reachability via Lipschitz Constants Craig Knuth Glen Chou N. Ozay Dmitry Berenson 24 33 0 18 Oct 2020
Adversarial Boot Camp: label free certified robustness in one epoch Ryan Campbell Chris Finlay Adam M. Oberman AAML 25 0 0 05 Oct 2020
Enhancing Mixup-based Semi-Supervised Learning with Explicit Lipschitz Regularization P. Gyawali S. Ghimire Linwei Wang AAML 31 7 0 23 Sep 2020
Adversarially Robust Neural Architectures Minjing Dong Yanxi Li Yunhe Wang Chang Xu AAML OOD 42 48 0 02 Sep 2020
Sampling-based Reachability Analysis: A Random Set Theory Approach with Adversarial Sampling T. Lew Marco Pavone AAML 30 53 0 24 Aug 2020
Optimizing Information Loss Towards Robust Neural Networks Philip Sperl Konstantin Böttinger AAML 18 3 0 07 Aug 2020
Adversarial Examples on Object Recognition: A Comprehensive Survey A. Serban E. Poll Joost Visser AAML 27 73 0 07 Aug 2020
AdvFoolGen: Creating Persistent Troubles for Deep Classifiers Yuzhen Ding Nupur Thakur Baoxin Li AAML 24 3 0 20 Jul 2020
Interpreting and Disentangling Feature Components of Various Complexity from DNNs Jie Ren Mingjie Li Zexu Liu Quanshi Zhang CoGe 19 18 0 29 Jun 2020
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness Jeremiah Zhe Liu Zi Lin Shreyas Padhy Dustin Tran Tania Bedrax-Weiss Balaji Lakshminarayanan UQCV BDL 37 437 0 17 Jun 2020
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger Jingfeng Zhang Xilie Xu Bo Han Gang Niu Li-zhen Cui Masashi Sugiyama Mohan S. Kankanhalli AAML 33 397 0 26 Feb 2020
Semialgebraic Optimization for Lipschitz Constants of ReLU Networks Tong Chen J. Lasserre Victor Magron Edouard Pauwels 36 3 0 10 Feb 2020
Softmax-based Classification is k-means Clustering: Formal Proof, Consequences for Adversarial Attacks, and Improvement through Centroid Based Tailoring Sibylle Hess W. Duivesteijn Decebal Constantin Mocanu 20 12 0 07 Jan 2020
There is Limited Correlation between Coverage and Robustness for Deep Neural Networks Yizhen Dong Peixin Zhang Jingyi Wang Shuang Liu Jun Sun Jianye Hao Xinyu Wang Li Wang J. Dong Ting Dai OOD AAML 21 32 0 14 Nov 2019
Robustness Guarantees for Deep Neural Networks on Videos Min Wu Marta Z. Kwiatkowska AAML 19 22 0 28 Jun 2019
Lower Bounds for Adversarially Robust PAC Learning Dimitrios I. Diochnos Saeed Mahloujifar Mohammad Mahmoody AAML 27 26 0 13 Jun 2019
Evaluating the Robustness of Nearest Neighbor Classifiers: A Primal-Dual Perspective Lu Wang Xuanqing Liu Jinfeng Yi Zhi-Hua Zhou Cho-Jui Hsieh AAML 23 22 0 10 Jun 2019
Scaleable input gradient regularization for adversarial robustness Chris Finlay Adam M. Oberman AAML 16 77 0 27 May 2019
POPQORN: Quantifying Robustness of Recurrent Neural Networks Ching-Yun Ko Zhaoyang Lyu Tsui-Wei Weng Luca Daniel Ngai Wong Dahua Lin AAML 17 75 0 17 May 2019
Plug-and-Play Methods Provably Converge with Properly Trained Denoisers Ernest K. Ryu Jialin Liu Sicheng Wang Xiaohan Chen Zhangyang Wang W. Yin AI4CE 22 348 0 14 May 2019
Evaluating Robustness of Deep Image Super-Resolution against Adversarial Attacks Jun-Ho Choi Huan Zhang Jun-Hyuk Kim Cho-Jui Hsieh Jong-Seok Lee AAML SupR 24 69 0 12 Apr 2019
Algorithms for Verifying Deep Neural Networks Changliu Liu Tomer Arnon Christopher Lazarus Christopher A. Strong Clark W. Barrett Mykel J. Kochenderfer AAML 36 392 0 15 Mar 2019
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 17 1,992 0 08 Feb 2019
PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach Tsui-Wei Weng Pin-Yu Chen Lam M. Nguyen M. Squillante Ivan Oseledets Luca Daniel AAML 21 30 0 18 Dec 2018
Wireless Network Intelligence at the Edge Jihong Park S. Samarakoon M. Bennis Mérouane Debbah 21 518 0 07 Dec 2018
MixTrain: Scalable Training of Verifiably Robust Neural Networks Yue Zhang Yizheng Chen Ahmed Abdou Mohsen Guizani AAML 21 23 0 06 Nov 2018
Efficient Neural Network Robustness Certification with General Activation Functions Huan Zhang Tsui-Wei Weng Pin-Yu Chen Cho-Jui Hsieh Luca Daniel AAML 11 743 0 02 Nov 2018
On Extensions of CLEVER: A Neural Network Robustness Evaluation Algorithm Tsui-Wei Weng Huan Zhang Pin-Yu Chen A. Lozano Cho-Jui Hsieh Luca Daniel 28 10 0 19 Oct 2018
Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation Chaowei Xiao Ruizhi Deng Bo-wen Li Feng Yu M. Liu D. Song AAML 16 99 0 11 Oct 2018
Improved robustness to adversarial examples using Lipschitz regularization of the loss Chris Finlay Adam M. Oberman B. Abbasi 24 34 0 01 Oct 2018
Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models D. Su Huan Zhang Hongge Chen Jinfeng Yi Pin-Yu Chen Yupeng Gao VLM 40 388 0 05 Aug 2018
On the Robustness of Interpretability Methods David Alvarez-Melis Tommi Jaakkola 30 522 0 21 Jun 2018
Lipschitz regularity of deep neural networks: analysis and efficient estimation Kevin Scaman Aladin Virmaux 22 516 0 28 May 2018
Towards Fast Computation of Certified Robustness for ReLU Networks Tsui-Wei Weng Huan Zhang Hongge Chen Zhao Song Cho-Jui Hsieh Duane S. Boning Inderjit S. Dhillon Luca Daniel AAML 33 686 0 25 Apr 2018
Gradient Masking Causes CLEVER to Overestimate Adversarial Perturbation Size Ian Goodfellow AAML 11 36 0 21 Apr 2018
Generative Adversarial Perturbations Omid Poursaeed Isay Katsman Bicheng Gao Serge J. Belongie AAML GAN WIGM 31 351 0 06 Dec 2017
Towards Robust Neural Networks via Random Self-ensemble Xuanqing Liu Minhao Cheng Huan Zhang Cho-Jui Hsieh FedML AAML 43 418 0 02 Dec 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 249 1,838 0 03 Feb 2017
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 296 3,112 0 04 Nov 2016
Safety Verification of Deep Neural Networks Xiaowei Huang Marta Kwiatkowska Sen Wang Min Wu AAML 180 932 0 21 Oct 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 293 5,842 0 08 Jul 2016